Residual Networks (ResNet): Transforming Deep Learning Models

The field of deep learning has been advancing at a very high rate. There are constant breakthroughs in this space, which allow us to make our deep learning algorithms more efficient to train and accurate at the same time. One such breakthrough is the concept of ResNet or residual networks in deep learning.

A few keywords to know before getting started with ResNet

Here are some things you should know before delving into ResNet. You should be familiar with some keywords and concepts to understand the following tutorial better. In this section of the tutorial, I have discussed some of them.

• Convolutional Neural Network (CNN): CNN is a deep learning model made to handle structured grid-like data, such as photographs. 
• Deep Learning: It is a subset of machine learning that uses neural networks with numerous layers, which are also called deep architectures, to extract complicated patterns and representations from datasets.
• Vanishing Gradient problem: This problem occurs when gradients in deep networks become extremely small during backpropagation. This makes it impossible to train very deep models efficiently as the accuracy drops instead of going up with the increase of layers.
• Residual Learning: A ResNet strategy that uses shortcut connections (residual connections) to allow gradients to flow more easily during training, hence minimizing the vanishing gradient problem.
• Skip Connection: A shortcut connection, also known as a direct connection between layers in a neural network, avoids one or more intermediate layers, hence improving gradient flow and facilitating deep network training.
• Feature Map: A spatial arrangement of features extracted by convolutional layers in a neural network, representing patterns and structures in the input data.
• Batch normalization: A method for normalizing and stabilizing the activations of intermediary layers in a neural network during training, enhancing convergence and improving training.
• ReLU (Rectified Linear Unit): An activation function commonly used in neural networks that adds nonlinearity by directly outputting positive inputs and zeros otherwise.
• Transfer learning: It is when the results of a previous training model can be used on a newer model with the same features or dataset, typically beginning with pre-trained models such as ResNet.
  

What is ResNet?

You might be wondering, ‘what is ResNet in deep learning’. Let me first give you a brief idea of what ResNet is all about. ResNet, or Residual Networks, is a significant architecture in the field of deep learning and neural networks. It introduced the concept of residual learning and changed how deep networks are designed and trained.

ResNet was created by Kaiming He and his team at Microsoft Research. It has become one of the most popular architectures for computer vision tasks such as image classification, object detection, and image segmentation. Its ability to train very deep networks efficiently while taking care of the vanishing gradient problem has made it a popular choice among researchers and practitioners in the field.

Components in a Residual Neural Network

Now, let me lay out all the components that make up the Residual Network in deep learning.

Input data

The input to a ResNet is usually an image or a data tensor containing the input features.

Convolutional layers

ResNet consists of several convolutional layers that extract features from input data. These layers use convolutional processes to apply learnable filters and detect spatial patterns in the data.

Batch normalization

Batch normalization layers are frequently used after convolutional layers to normalize activations, which improves training stability and convergence speed.

Activation functions (ReLU)

Rectified Linear Units (ReLU) are widely employed as activation functions following convolutional and batch normalization layers to add nonlinearity to the network.

Residual blocks

ResNet's key component is the residual block. A residual block has two major paths:

• Identity path: This path, also known as shortcut connection, connects the input (or an altered version of it) to the block's output using a skip or shortcut link. It aids in maintaining information from previous layers and addressing the vanishing gradient issue.
• Convolutional path: This approach uses convolution and other modifications on the input to learn residual mappings. This path's output is combined element by element with the output of the identity path.
  

Stacked residual blocks

ResNet often stacks several residual blocks to form deep structures. The network depth (number of stacked blocks) can be adjusted according to the desired model complexity and task requirements.

Pooling layers

Pooling layers, such as average or max pooling, can be used to downsample feature maps and minimize spatial dimensions, which are commonly employed in classification tasks.

Fully connected layers

Fully connected layers are used in the network's final layers (which are not always included in all ResNet variations) for tasks such as classification and mapping the learned features to output classes or predictions.

Advantages of ResNet

ResNet algorithms helped overcome many inherent problems with the previous CNN models. Let me briefly mention ResNet's advantages.

Effective training of deep neural networks

ResNet's main advantage is its ability to train extremely deep neural networks effectively. Deep networks frequently suffer from the vanishing gradient problem, in which gradients decrease in size as they propagate backward through multiple layers during training. 

ResNet addresses this issue with residual connections, also known as skip connections. These connections make gradients flow more smoothly by offering shortcut channels for information to bypass specific layers.

Improved accuracy

ResNet architectures are often more accurate than shallower networks or architectures without residual connections. The capacity to train deep networks effectively allows ResNet models to capture more complicated patterns and features from data, resulting in improved generalization and classification performance. 

Scalability

ResNet's architecture is extremely scalable, allowing researchers and practitioners to create networks with variable depths based on job needs and available computational resources. Its scalability makes it suitable for a wide range of applications, from small-scale projects to large-scale deployments requiring very complicated convolutional neural network models

Transfer learning

ResNet models that have been pre-trained on huge datasets, such as ImageNet, are widely available. This makes ResNet an excellent tool for transfer learning, in which knowledge learned from pre-training on one task or dataset may be transferred and fine-tuned for related tasks or smaller datasets. 

Transfer learning using ResNet may significantly decrease development time and enhance model performance, especially in cases with limited training data.

Versatile in nature

ResNet is versatile and can be used for a variety of computer vision applications like picture classification, such as object identification, image segmentation, and recognition.

In Conclusion

ResNet models have changed how we tackle certain problems in deep learning. They help us create more efficient and powerful models based on which smarter artificial intelligence can be fashioned. This tutorial should have given you a pretty solid idea about the concept of ResNet algorithms in deep learning.

If you want to learn more advanced topics like ResNet in detail, I recommend taking a relevant course on it from a trusted and certified platform. You can check out courses from upGrad. Their courses are some of the best in the business. They are curated by some of the best professors in the field and are offered in collaboration with premier universities around the world.

Frequently Asked Questions

1. What is ResNet used for?

ResNet is primarily used in computer vision tasks such as image classification, object detection, and segmentation. Its deep architecture and efficient training methods make it ideal for dealing with complex visual data and achieving top-tier performance in these tasks.

2. How many layers are there in ResNet?

ResNet comes in a variety of depths, from a few layers (e.g., ResNet-18) to extremely deep networks (e.g., ResNet-152). The original ResNet paper described ResNet-50, ResNet-101, and ResNet-152, which have 50, 101, and 152 layers, respectively. However, the ResNet architecture is adaptable, allowing for different configurations and depths depending on the application's needs.

3. Why ResNet is better than others?

ResNet's superiority comes from its ability to train very deep networks effectively, thanks to residual connections that reduce the vanishing gradient problem. This results in higher performance and scalability than other architectures, making it a top choice for many computer vision tasks.

4. What is the difference between VGG and ResNet?

The key difference between VGG and ResNet is in their architectures and training methods. VGG is defined by its uniform architecture of repeated convolutional layers, whereas ResNet incorporates residual connections that facilitate training and allow for the creation of much deeper networks without vanishing gradient issues.

5. What are the advantages of ResNet?

ResNet excels at training deep networks with residual connections while overcoming the vanishing gradient problem. This leads to greater accuracy, particularly in complex tasks. Its scalability allows for different network depths, making it suitable for a variety of computer vision applications.

6. Why ResNet is better than VGG?

ResNet outperforms VGG because of its ability to train deeper networks effectively with residual connections, which prevents the vanishing gradient problem. ResNet outperforms VGG in terms of accuracy and scalability.

7. Which is faster, VGG or ResNet?

In terms of inference speed, VGG generally outperforms ResNet. This is because VGG has a simpler architecture with fewer layers than ResNet, allowing it to perform computations faster during inference. The trade-off is that ResNet frequently achieves higher accuracy, particularly in very deep models, despite being slightly slower in inference.

8. Which is best, VGG or ResNet?

The decision between VGG and ResNet is based on the specific use case and trade-offs. VGG is simpler and faster, although it may have poorer accuracy than ResNet. With its deeper architecture and residual connections, ResNet frequently delivers higher accuracy but may be slower to infer. ResNet is preferred for many tasks due to its higher performance in deep learning applications.